vanessa joglar biological oceanography group
TRANSCRIPT
Vanessa Joglar
Biological Oceanography Group
Koji Suzuki
Associate Editor
Biogeosciences
Vigo, 29th November 2019
Dear Koji
Please find attached a revised version of manuscript entitled “Spatial and temporal
variability in the response of phytoplankton and bacterioplankton to B-vitamin amendments in an
upwelling system”. The manuscript was co-authored by myself, Antero Prieto, Esther Barber-
Lluch, Marta Hernández-Ruíz, Emilio Fernandez and Eva Teira.
We would like to acknowledge the insightful and constructive comments of the reviewers,
which clearly helped us to improve the overall merit of the manuscript. We have taken into
account all the suggestions raised resulting in a higher quality work. We attach a detailed response
to all the comments made by the reviewers. In the individual responses to referee comments
(RCs), the suggestions and comments of the individual reviewers are in plain font and our
responses are in italics and blue font. The major changes are summarized below:
• Both referees agreed that the calculated response ratios could be useful in the discussion
and that the actual chlorophyll-a and bacterial biomass data at the beginning and after the
72 h incubation in all the treatments should be shown. As we agree that this information
is of great value, we have prepared two new figures (Fig. 5 and 6) where we plot all the
requested information. In the revised version, the former figure 5, showing the response
ratios has been included as supplementary information.
• Another suggestion by both referees was to provide details about the methodology related
to B12 measurement. This section has been expanded by referring to previous work and
indicating the particular conditions and instruments used in our laboratory.
• We have fully reviewed the statistical analyses. We now better explain the RELATE
analyses. We have also reconsidered the usefulness of the Pearson's correlation analyses
between the individual responses to vitamins and prokaryotic taxa (summarized in former
table 1). We believe that this analyses could be redundant and its conclusions somewhat
speculative. Based on this, we have removed this analysis, eliminated the former table 1,
and fully reviewed the discussion, eliminating speculative statements, and toning down
some of our conclusions.
• The genomic data of this study will be publicly available at the European Nucleotide
Archive (ENA) at EMBL-EBI (https://www.ebi.ac.uk/ena) as soon as possible.
Looking forward to hearing from you,
Vanessa Joglar
Full address for corresponding is:
Grupo de Oceanografía Biológica
Departamento de Ecología y Biología Animal
Universidad de Vigo
Campus Universitario Lagoas-Marcosende
36310-Vigo Spain
E-mail: [email protected]
#Referee1 comments
We very much appreciate the useful and constructive comments made by the reviewer,
which surely contribute to improve the manuscript quality. We have considered all the
suggestions and made the requested modifications, as detailed below.
My main initial request is to include figures for the actual bacterial and phytoplankton
biomass changes in the experiments, rather than simply ratios, including the values for
the initial conditions. I believe this should be in the main manuscript, not just the
Supporting Information. These data can be displayed as a mean with error bars
representing the spread across the three treatment replicates. I believe this will give a
better indication of how the community responded in the experiments. The ratio figures
can be included too for discussion/interpretation purposes. Please also label the
treatments below each bar in each case – I found treatment identification a little difficult
in the current figures.
We have now included in the main manuscript the requested figure (new figure 5 and 6).
We present the response of phytoplankton and that of bacteria separately. The former
figure 5 with the response ratios is now included as supplementary information.
Secondly I think the manuscript should also note how trace metal contamination could
have biased the results. This is currently not discussed at all, but could have had an
important influence. For instance, if contaminating iron had been inadvertently included
in the treatments. Contamination would likely originate from the metal CTD-rosette, the
rosette bottles, during bottle sampling, from the incubation bags, from the nutrient
additions etc. Where certain procedures were carried out to reduce this, these should be
described. This is significant, as this microbes in this region could be experiencing
primary iron limitation – see Blain et al. (2004).
Blain, S., Guieu, C., Claustre, H., Leblanc, K., Moutin, T., Quéguiner, B., Ras, J. and
Sarthou, G., 2004. Availability of iron and major nutrients for phytoplankton in the
northeast Atlantic Ocean. Limnology and Oceanography, 49(6), pp.2095-2104.
We did not use trace metal clean techniques for sampling. We used standard stainless
CTD-rosette and Niskin metal-free bottles. We did not have a trace metal clean lab on
board, so, even though samples were carefully manipulated contamination by trace
metals could have eventually occurred. It is important to note that the water for the
experiments was pooled into a 20 l acid-cleaned carboy before filling the bags (L134-
L136), thus all the bags would have the same incidental input of trace metals. We are
aware that microbes could be limited by other trace elements or nutrients not considered
in our treatments, such as iron or other B vitamins. For this reason, we based the
discussion on the response ratios at the end of the experiments.
Specific comments
In the abstract I would recommend making reference to the study region (i.e. ‘North east
Atlantic’, or ‘off the northwest coast of Spain’)
The study area has been specified in the abstract (L15)
Figure 1b and c: please indicate when experiments were sampled for (i.e. which day?
day 0?)
Sampling day for each experiment has been indicated in the graphs (Fig. 1b and Fig. 1c)
I would recommend noting the microbial responses to major nutrient supply, in addition
to B12/B1, in the abstract.
The response to inorganic nutrient additions has been considered in the abstract (L13-
L14)
Line 15–16: rephrase ‘was not of great concern’
This has been rewritten (L15)
I would recommend stating the number of the 36 experiments where bacteria/
phytoplankton responded positively/negatively to vitamin supply in the abstract.
This information has been included in the abstract (L16-L19)
Line 21 ‘Growth stimulation by B1 addition was more frequent on bacteria’ – relative to
phytoplankton?
This has been clarified (L22-L23)
Lines 35–36 and elsewhere: I would recommend seeing the more recent studies of
Browning et al., 2017 and Browning et al., 2018, which also perform trace-metal-clean
B12 addition bioassay experiments in upwelling/coastal/offshore regions.
Browning, T.J., Achterberg, E.P., Rapp, I., Engel, A., Bertrand, E.M., Tagliabue, A. and
Moore, C.M., 2017. Nutrient co-limitation at the boundary of an oceanic gyre. Nature,
551(7679), p.242.
Browning, T.J., Rapp, I., Schlosser, C., Gledhill, M., Achterberg, E.P., Bracher, A. and
Le Moigne, F.A., 2018. Influence of iron, cobalt, and vitamin B12 supply on phytoplankton
growth in the tropical East Pacific during the 2015 El Niño. Geophysical Research
Letters, 45(12), pp.6150-6159.
Both studies have been cited in the revised version (L38)
Line 39: synthesized by prokaryotes and archaea?
Archaea is included within the prokaryote organisms. This has been clarified in the
manuscript (L41)
Line 42: Have not defined ‘cobalamin’ (In general I recommend choosing B12 or
cobalamin and sticking to it throughout)
This has been corrected (L45)
Line 79: Perhaps mention here succinctly what Gobler et al. (2007) found?
This has been explained in the revised text (L83-L84)
Line 79: the reference Barber-Lluch et al. (2019) does not appear in the reference list
â˘A.
This citation has been included in the reference list (L620-L622)
Lines 114–115: How was this water sampled? From the regular stainless CTD? If so,
trace element contamination should be acknowledged. Also see general comment.
This has been acknowledged in the revised manuscript (L120; L134-L136)
Line 125: Was there any treatment of the whirl-pak bags (e.g. acid and deionized water
rinses) to remove contamination? Also see general comment.
We used these bags mainly because they are sterile, non-toxic and transparent to the
whole solar spectrum, thus avoiding UVR absorption of most other materials, and have
been frequently used for experimentation with plankton communities (Gonzalez et al.,
1990; Davidson and van der Heijden, 2000; Pakulski et al., 2007; Teixeira et al., 2018).
The bags were not additionally treated as were used only once.
Line 127 and on: What were the chemical stocks of the nutrients (e.g. brand and purity).
Again, if these nutrients were not pre-treated to remove trace element contamination,
this should be acknowledged. Also see general comment.
All the reagents were from Sigma of highest purity. Stocks were prepared with
autoclaved Milli-Q water. No additional treatment for trace element contamination was
applied.
Line 137: Were the tanks screened, or open to the air?
Tanks were screened to attenuate light intensity (L148-L150)
Line 147: Was any time given for the fixative to act on cells before flash freezing in liquid
nitrogen?
Samples were incubated 20 min for fixative to act (L160-L161)
Section 2.5: If known, what was the recovery percent of the B12 preconcentration/
extraction? (i.e. via use of a standard)
Average B12 recovery percentage was 93% (L213-L214)
Line 271: How was the upwelling index calculated (cannot see this in methods).
Upwelling index was calculated by calculating the Ekman transport from surface winds
at fix-station (st3) located at 42º N and 8.88º W. This information has been included in
the revised text (L129-L132)
Figure 5: It is not clear that the value being displayed is the RR Chla OR RR BB and not
the ratio of these.
This has been clarified (Fig. S3 in the supplement)
As the Figure 5 has signs indicating statistical significance, the error in the spread across
the treatment replicates must have been prorogated somehow? Can this error be
included as error bars in the figure?
The error bars representing the standard error of the three replicates have been included
in the new figure 5 (now Figure 5 and 6).
337–338: Specifically which experiments showed serial limitation by B vitamins?
This has been specified in the revised manuscript (L387-L390)
Line 402: ‘clarify the paper of vitamins’?
This fragments has been corrected for clarity (L444)
Lines 417–419: Please distinguish between the phytoplankton/bacteria responses in this
value of 75%.
Taking into account the responses of phytoplankton and bacteria separately, the
percentages were 75% for phytoplankton and 50% for bacteria (L457-L548)
Line 425: No full stop (perhaps also rephrase to ‘community assemblage’?)
This sentence was removed.
Lines 491–495: This doesn’t quite make sense – in the first sentence it states that
phytoplankton responses to B1 supply were restricted, and in the second the stimulation
of phytoplankton is discussed.
Phytoplankton responses to B1 are overall restricted. The second sentence refers to the
particular simultaneous stimulation of phytoplankton and bacteria by B1 addition found
in subsurface oceanic waters in February (L532-L533)
I would advise including a table summarizing initial conditions (i.e., nutrient
concentrations, temperature, chlorophyll-a, initial bacteria and so on).
We have added a supplementary table including detailed information about initial
conditions (Table S2 in the supplement)
In addition to the modifications suggested by the reviewer, we have made the following
changes. The named OTUs (operation taxonomic units) has been replaced by ASV
(amplicon sequence variant) due to the sequence analysis method used DADA2 (section
2.6).
We have eliminated the Pearson correlation between response ratios and the clr
(centered-log-ratio) abundance of taxonomic groups (reported in former table 1) as was
redundant with the dbRDA. Regarding the RELATE analysis to explore the relationship
between the responses to B vitamin treatments (response ratios of phytoplankton and
bacteria) and (1) the environmental variables (including nutrients, temperature, salinity,
B12, chla and BB), (2) the prokaryotic, or (3) eukaryotic community structure, we believe
that nicely shows that the responses are only significantly related to the prokaryotic
community structure.
#Referee 2 comments
We are very grateful for the reviewer's comments, which have contributed to improve the
manuscript.
The role that the availability of B-vitamins,specifically vitamin B12 and B1,play in shaping
the marine microbial community is very relevant. The authors of this manuscript
conducted an extensive experimental campaign with the goal of providing some insight
to these processes. Unfortunately, their findings are poorly communicated and
overstated in this manuscript. Most of the discussion is highly speculative and is
insufficiently referenced.
We have made our best to refer adequately all the relevant studies and eliminating some
speculative statements.
The authors have gone “all-in” on the poorly justified concept of “response ratio”. I feel
like this calculated metric is overly general and prevents an in-depth analysis of the
actual data which likely contains subtle variations that could either support or undermine
the authors primary conclusions. I don’t understand why the authors chose to use
response ratios rather more traditional ecological and physiological metrics. While
response ratios could be a useful part of the discussion, they should be just that, a part
of the discussion. Additionally, the authors ignore the rates of community growth and
dynamics and only assess the response at the end time point relative to the initial point.
While it is not possible at this point to change the experimental design, the authors need
to change their interpretation of the data to acknowledge the limits of their data.
We are aware that sampling only at one endpoint (after 72 h incubation in our case) does
not allow to discuss in detail the dynamics during each experiment, however we were
particularly interested in extensively exploring the temporal and spatial variability of the
response to vitamin enrichment. The experimental design, involved 36 experiments, with
8 triplicate treatments (24 experimental units per experiment). Even sampling only at the
beginning and at the end we collected 972 samples for chlorophyll-a, and 972 for
bacterial biomass. Initial and endpoint sampling is a common practice in enrichment
microcosm experiments (e. g. Mills et al., 2004; Moore et al., 2006; Gobler et al., 2007;
Bonnet et al., 2008; Koch et al., 2011), and allows the estimation of net growth rates
using the following formula: ln (endpoint biomass/initial biomass)/incubation time. A
previous work by Barber-Lluch et al (2019) in the same sampling area allowed us to
conclude that sampling at 72 h was adequate to explore the effect of vitamins on both
phytoplankton and bacterial biomass. As we agree with the referee that the dynamics of
phytoplankton and bacteria during the experiments are of interest, and following also the
advice of referee 1, we now include in the manuscript two new figures where the initial
and endpoint value of chlorophyll-a and bacterial biomass is represented. The response
ratio figure is now included in the supplementary information. We accordingly now
describe the dynamics of both planktonic components in the different experiments. We
nevertheless decided to keep the response ratio as a measure of the magnitude of the
effect (see below), which is very useful for the sake of comparison.
We used here the response ratio as the quotient between the measured quantity of a
response variable in experimental and control experimental units. Previous studies
dealing with the effects of nutrients additions on microbial communities have noted the
importance of expressing the change in the treatment relative to the control (Downing et
al., 1999; Hedges et al., 1999; Elser et al., 2007, among others). We find that this variable
is particularly adequate as a measure of the experimental effect because it quantifies the
proportionate change that results from an experimental manipulation. This metric is
widely use in marine ecology, and particularly in nutrient amendment experiments (e.g.
Martínez-García et al., 2010; Teira et al., 2013; Barber-Lluch et al., 2019). The use of
the response ratio calculated from endpoint biomass data provides the same information
as the comparison of growth rates between treatments. Below we plot, as an example,
the relationship between the response ratio from biomass (endpoint biomass in treatment
divided by endpoint biomass in control) and the difference between growth rates (growth
rate in treatment minus growth rate in the control) using data from two of our experiments
(represented with different colours). It can be appreciated that the information provided
by the response ratios follows exactly the same pattern as that provided by comparing
growth rates. Moreover, the range of variation is higher for the response ratio, which
allows to statistically detecting more subtle changes.
It is unfortunate that the only measures of biomass performed by these authors during
their experiments were bacterial abundance and chlorophyll A. These are very broad,
unspecific measures of community structure, that can be impacted by a myriad of
environmental factors. The authors make some substantial claims about the roles that
B-vitamin additions are playing on the microbial community; however, I wonder if they
really have enough resolution in their measurements to make these claims. The author’s
use of “response rate” to obscures the fact that they are only measuring bacterial
abundance and chlorophyll concentration. There are so many variables that impact these
measures, it’s not clear to me that the authors are actually looking at responses from B-
vitamins.
B vitamins are essential growth factors for all microorganisms; therefore, the ultimate
effect of a vitamin deficiency will be an impairment of growth, which is typically evaluated
from changes in biomass. It is true that we only measure the effect on bulk phytoplankton
and bacteria, and thus we have toned down all the conclusions about the effect on
microbial community structure. We do not think that is unfortunate to have chosen
phytoplankton and bacterial biomass as response variable, considering that most
previous studies evaluating the role of B vitamins were based on biomass
measurements (Sañudo-Wilhelmy et al., 2006; Gobler et al., 2007; Koch et al., 2011,
2012; Browning et al., 2018; Barber-Lluch et al., 2019). We are aware of many variables
that could affect bacterial and phytoplankton biomass, for this reason, we compared the
response of B vitamin treatments with their corresponding controls. B12, B1 and B12+B1
treatments were compared to the unamended control, while I+B12, I+B1, I+B12+B1 were
compared with the I treatment.
I have some substantial concerns about the conclusions the authors make about
community diversity and B-vitamins. Their exact statistical methods need to be better
explained. Additionally, the authors need to fully explain the limits of their statistical
methods, and not overstate or be overly speculative about the observed correlations
between abiotic/biotic factors, B-vitamins, and the amplicon data. The manuscript needs
substantial copy editing/English language editing. All sections need to be streamlined.
The interpretation of results tends to be far too speculative. The authors need to only
make claims that their data can support.
We agree that some statistical methods needed further clarification and we recognize
that some analyses were somehow redundant and have been excluded from this revised
version. Specifically, we have eliminated the Pearson correlation between response
ratios and the clr (centered-log-ratio) abundance of taxonomic groups (reported in former
table 1) as was redundant with the dbRDA. Regarding the RELATE analysis to explore
the relationship between the responses to B vitamin treatments (response ratios of
phytoplankton and bacteria) and (1) the environmental variables (including nutrients,
temperature, salinity, B12, chla and BB), (2) the prokaryotic, or (3) eukaryotic community
structure, we believe that nicely shows that the responses are only significantly related
to the prokaryotic community structure. We have clarified how we constructed the
resemblance matrices (L273-L283).
It is important to note, that as we are aware of the statistical limitations when working
with relative abundance of sequences, prior to statistical analyses, ASV abundances
were transformed using the centered log ratio (Fernandes et al., 2014; Gloor et al., 2017).
The B12 analytical method appears to be derived from previously published methods.
Specifically, those published by Heal et al. 2014, Sañudo et al. 2012, and Suffridge et al.
2017. It is troubling to me that the authors do not cite any of these papers in the methods
section, despite the fact that the described method is a nearly an exact match of those
described in the above papers. Additionally, SPE extraction efficiency and limits of
detection need to be included.
We now provided all the requested details about the vitamin B12 quantification method
and included all the references (Section 2.5, L191-L194, L205-L215).
How were the whirl-pak bags prepared? Were they prepared to be trace clean? Were
they sterile? What sort of plastic are they made out of? Trace metal or trace organic (B-
vitamin) contamination is a real concern in experiments like these, especially when the
authors want to make conclusions about the impact of a trace-component. Many plastics
contain trace contamination from the factory, and if the bags were not properly prepared,
this variability could interfere with all results.
We did not use strict trace metal clean techniques for sampling. It is important to note
that the water for the experiments was pooled into a 20 l acid-cleaned carboy before
filling the bags, thus all the bags would have the same incidental input of trace metals.
We are aware that microbes could be limited by other trace elements or nutrients not
considered in our treatments, such as iron or other B vitamins. For this reason, we based
the discussion on the response ratios at the end of the experiments.
The whirl-pak® bags are made of low density polyethylene, are sterile, non-toxic and
transparent to the whole solar spectrum, thus avoiding UVR absorption of most other
materials, and have been frequently used for experimentation with plankton communities
(Gonzalez et al., 1990; Davidson and van der Heijden, 2000; Pakulski et al., 2007;
Teixeira et al., 2018). The bags were not additionally treated as were used only once.
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1
Spatial and temporal variability in the response of 1
phytoplankton and bacterioplankton to B-vitamin 2
amendments in an upwelling system 3
Vanessa Joglar1*, Antero Prieto1, Esther Barber-Lluch1, Marta Hernández-Ruíz1, Emilio 4 Fernandez1 and Eva Teira1 5
1 Departamento Ecoloxía e Bioloxía Animal, Universidade de Vigo, Campus Lagoas-Marcosende, Vigo, 6 36310, Spain 7
*Correspondence to: Vanessa Joglar +34 986 818790 ([email protected]) 8
9
2
Abstract. We evaluated the temporal (inter-day and inter-season) and spatial variability 10
in microbial plankton responses to vitamins B12 and B1 supply (also in combination with 11
inorganic nutrients) in coastal and oceanic waters of the northeast Atlantic ocean. 12
Phytoplankton and, to a lesser extent, bacteria were strongly limited by inorganic 13
nutrients. Inter-day variability in microbial plankton responses to B-vitamins was 14
unimportant, suggesting that B-vitamins availability was controlled by factors operating 15
at larger temporal scales. Phytoplankton and bacteria positively responded to B-vitamin 16
amendments in 13 % and 21 %, respectively, of the 216 cases (36 experiments x 6 17
treatments). Negative responses represented 21 % for phytoplankton and 26 % for 18
bacteria. Most positive responses were produced by treatments containing either B12 19
alone or B12 combined with B1 in oceanic waters, which was consistent with the 20
significantly lower average vitamin B12 ambient concentrations compared to that in the 21
coastal station. Growth stimulation by B1 addition was more frequent on bacteria than in 22
phytoplankton, which is coherent with their widespread dependence on exogenous 23
sources of this growth factor. Negative responses to B-vitamins were generalized in 24
coastal waters in summer, and were associated to a high contribution of Flavobacteriales 25
to the prokaryote community. This observation suggests that the external supply of B12 26
and/or B1 may promote negative interactions between microbial components when B-27
vitamin auxotrophs are abundant. The microbial response patterns to B12 and/or B1 28
amendments were significantly correlated with changes in the prokaryotic community 29
composition, highlighting the pivotal role of prokaryotes in B-vitamins cycling in marine 30
ecosystems. 31
1 Introduction 32
Phytoplankton accounts for almost half of the global net primary production (Field et al., 33
1998) and may eventually cause toxic episodes entailing human health problems and large 34
economic losses (Hallegraeff, 1993; van Dolah et al., 2001). Recent emerging evidence 35
suggests the role of biologically active organic compounds, such as B-vitamins, on the 36
control of marine productivity in both coastal and oceanic waters (Panzeca et al., 2006; 37
Bertrand et al., 2007; Gobler et al., 2007; Koch et al., 2011; Browning et al., 2017, 2018). 38
B-vitamins act as cofactors for enzymatic reactions and are involved in many important 39
metabolic pathways (Madigan et al., 2005; Koch et al., 2011; Monteverde et al., 2017). 40
Vitamin B12 (B12 herein), which is exclusively synthesized by some bacteria and archaea 41
3
(Roth et al., 1996; Martens et al., 2002; Warren et al., 2002), acts as a cofactor of three 42
enzymes in eukaryotes (methionine synthase, methylmalonyl-coA mutase and 43
ribonucleotide reductase type II) (Helliwell et al., 2011; Bertrand and Allen, 2012). In 44
comparison, over 20 different B12-dependent enzymes are found in bacteria (Roth et al., 45
1996), making B12 critically important also for these organisms. Vitamin B1 (B1 herein) 46
plays a pivotal role in intermediary carbon metabolism and is a cofactor for a number of 47
enzymes involved in primary carbohydrate and branched-chain amino acid metabolism 48
(Croft et al., 2006). 49
Most eukaryote phytoplankton species are auxotrophs for one or more B-vitamins, 50
consequently requiring an exogenous supply of these molecules (Carlucci and Bowes, 51
1970; Haines and Guillard, 1974; Croft et al., 2005; Tang et al., 2010; Helliwell et al., 52
2011; Bertrand and Allen, 2012). Moreover, genomic data also indicate widespread B-53
vitamins auxotrophy among many bacterial taxonomic groups (Sañudo-Wilhelmy et al., 54
2014; Paerl et al., 2018), which implies that phytoplankton and bacteria may eventually 55
compete for the acquisition of these compounds (Koch et al., 2012). Auxotrophic 56
microorganisms may acquire the required vitamins from the environment or through 57
biotic interactions with prototrophic (biosynthetically competent) microorganisms 58
(Droop, 2007; Grant et al., 2014; Kazamia et al., 2012). A well-known example is the 59
mutualistic interaction between B12-dependent phytoplankton and bacteria (Croft et al., 60
2005; Amin et al., 2012; Cooper and Smith, 2015). 61
Even though B-vitamins appear to be important and potentially limiting factors for 62
microbial plankton, our understanding of B-vitamins cycling in the ocean is largely 63
limited by the complex and still evolving analytical methodology for its quantification in 64
natural waters (Okbamichael and Sañudo-Wilhelmy, 2004, 2005; Suffridge et al., 2017). 65
Sañudo-Wilhelmy et al. (2012) found extensive areas of coastal waters with close to 66
undetectable B12 concentrations, suggesting that microbes might be well adapted to drive 67
under limiting conditions for this growth factor. 68
The factors limiting phytoplankton and bacterial growth in marine ecosystems are known 69
to vary over different spatial and temporal scales (Cullen et al., 1992; Arrigo, 2005; 70
Church, 2008; Saito et al., 2008; Martínez-García et al., 2010a, 2010b; Moore et al., 71
2013), in accordance with the dynamic nature of microbial communities (Pinhassi et al., 72
2003; Pommier et al., 2007; Fuhrman et al., 2008; Carlson et al., 2009; Hernando-Morales 73
et al., 2018; Hernández-Ruiz et al., 2018). Compared to mineral nutrient and trace 74
4
elements, much less is known about B vitamin limitation and its spatial and temporal 75
variability in marine ecosystems. 76
Some studies have shown enhanced phytoplankton biomass associated to B12 77
amendments in both temperate coastal and polar waters (Bertrand et al., 2007; Gobler et 78
al., 2007; Koch et al., 2011; Koch et al., 2012). The simultaneous effect of vitamin B12 79
supply on both phytoplankton and bacteria has been barely explored (Koch et al., 2011, 80
Barber-Lluch et al., 2019). To our knowledge, the effect of B1 amendments on marine 81
natural microbial plankton community succession has been only assessed by Gobler et al. 82
(2007), who suggested that high concentration of B-vitamins, associated with high 83
bacterial abundance, caused an increase in auxotrophs, mostly dinoflagellates. 84
The Ría de Vigo (NW Spain) is a coastal embayment affected by intermittent upwelling 85
of subsurface cold and inorganic nutrient-rich water from March to September and the 86
downwelling of open ocean surface water from October to March (Fraga, 1981; Barton 87
et al., 2015). In addition to this seasonality, fluctuations of wind patterns in the area 88
generate upwelling and downwelling events occurring within each season (Alvarez-89
Salgado et al., 1993; Figueiras et al., 2002). A recent study by Barber-Lluch et al. (2019) 90
at a shelf station off the Ría de Vigo (NW Spain) showed monthly variation in the 91
response of phytoplankton and bacteria to nutrient and/or B12 additions in surface waters, 92
likely related to variation in the ambient concentration of B12 and the taxonomic 93
community composition. Unfortunately, the role of these factors on the microbial 94
response to the amendments were not specifically assessed by these authors. 95
Within this context, the aim of our study was to explore spatial (horizontal and vertical) 96
and temporal (inter-day and inter-season) variability patterns in B12 and B1 vitamin 97
limitation in relation to the prevailing initial abiotic (e.g., nutrient and B12 98
concentrations) and biotic (eukaryote and prokaryote community composition) 99
conditions in this productive ecosystem. We conducted a total of 36 microcosm bioassays 100
in February, April, and August 2016 to evaluate the response of heterotrophic bacteria 101
and phytoplankton biomasses to the addition of B12 and/or B1. 102
Considering that a large fraction of eukaryotic phytoplankton and bacterial taxa require 103
exogenous B-vitamins and considering the different requirements and capabilities to 104
synthetize B-vitamins by different microbial taxa, we hypothesize that microbial 105
5
community composition play a relevant role in explaining B-vitamins limitation patterns 106
in microbial plankton. 107
2 Methods 108
2.1 Experimental design 109
Thirty-six enrichment experiments were performed in the upwelling system near Ría de 110
Vigo on board “B/O Ramón Margalef” in three different oceanographic cruises 111
(ENVISION I, II & III) conducted in 2016. Two different locations of the East Atlantic 112
Ocean, one coastal station (st3) (42º N, 8.88º W) and one oceanic station (st6) (42º N, 113
9.06º W) (Fig. 1), were sampled during three different seasons aimed to cover a wide 114
range of initial hydrographic and ecological conditions. The 10-day cruises were 115
conducted in February (ENVISION I), coinciding with the spring bloom, and April 116
(ENVISION II) and August (ENVISION III) during the early and late summer upwelling, 117
respectively. During each cruise, 12 enrichment experiments were carried out on board, 118
3 experiments in each station (3a, 3b & 3c and 6a, 6b & 6c, respectively) with water from 119
two different depths. Water was collected using 20 l Niskin metal-free bottles. Surface 120
and sub-surface chlorophyll maximum (SCM) samples were taken at 5 m and at the 121
maximum fluorescence depth, between 10 m and 50 m according to the CTD data, 122
respectively (Fig. 2). We failed to sample the SCM on two occasions, due to large vertical 123
displacements between the downward and the upward casts. Vertical profiles of 124
temperature, salinity and chlorophyll fluorescence were obtained using a regular stainless 125
CTD-rosette down to 60 m in the coastal station and to 200 m in oceanic station. Samples 126
for phytoplankton and bacterial biomasses, dissolved nutrient concentration, including 127
vitamin B12, and microbial plankton community were collected at the beginning of each 128
experiment. Daily upwelling index (UI) values were computed by the Instituto Español 129
de Oceanografía (www.indicedeafloramiento. ieo.es/) in a 2º x 2º geostrophic cell 130
centered at 42 ºN , 10 ºW, using data from atmospheric pressure at sea level, derived from 131
the WXMAP model (Gonzalez-Nuevo et al., 2014). 132
Seawater samples were gently pre-filtered through a 200 µm mesh to exclude large 133
zooplankton in order to ensure good replicability and collected into a 20 l acid-cleaned 134
polyethylene carboy. It is important to note that incidental trace-metal contamination 135
could have occurred during water collection. Following sample collection, 300 ml PAR 136
6
and UVR transparent, sterile, and non-toxic (whirl-pak) bags were filled and nutrients 137
were added establishing eight different enrichment treatments as follows: (1) control 138
treatment (C): no nutrients added; (2) inorganic nutrient treatment (I): 5 µM nitrate (NO3−), 139
5 µM ammonium (NH4+), 5 µM silicate (SiO4
2−) and 1 µM phosphate (HPO42−); (3) vitamin 140
B12 (Sigma, V2876) treatment: 100 pM; (4) vitamin B1 (Sigma, T4625) treatment: 600 141
pM); (5) Inorganic nutrients and vitamin B12 (I+B12) treatment; (6) Inorganic nutrients 142
and vitamin B1 (I+B1) treatment; (7) vitamins B12 and B1 (B12+B1) treatment and (8) 143
Inorganic nutrients with vitamins B12 and B1 (I+B12+B1) treatment. Inorganic nutrients 144
were added to avoid that inorganic nutrient limitation masked the responses to B vitamins. 145
Each treatment had 3 replicates resulting in 24 whirl-pack bags per experiment. To assess 146
short-term effects of nutrient inputs, experimental bags were incubated on-deck during 147
72 h under natural light conditions. In-situ temperature and light were reproduced by 148
submerging the bags in tanks connected to the surface-water pump system, and covered 149
with screens simulating the light intensity at the sampling depth. 150
2.2 Chlorophyll-a 151
Chlorophyll-a (Chl-a) concentration was measured at time-zero and after 72 h incubation 152
as a phytoplankton biomass proxy. 300 ml of water samples were filtered through 0.2 μm 153
polycarbonate filters and frozen at -20ºC until further analysis. Chl-a was extracted with 154
90 % acetone and kept in darkness at 4ºC overnight. Fluorescence was determined with a 155
TD-700 Turner Designs fluorometer calibrated with pure Chl-a (absorption coefficient at 156
665 nm = 12.6) standard solution. 157
2.3 Flow cytometry 158
Samples for heterotrophic bacteria abundance quantification (2 ml) were preserved with 159
1 % paraformaldehyde + 0.05 % glutaraldehyde (final concentrations). Samples were 160
incubated 20 min for the fixative to act on cells and frozen at -80ºC after 15 min. 161
immersion in liquid nitrogen. Abundance of heterotrophic bacteria was determined using 162
a FACSCalibur flow cytometer equipped with a laser emitting at 488nm. Samples were 163
stained with SYBR Green DNA fluorochrome, and bacterial abundance was detected by 164
their signature of side scatter (SSC) and green fluorescence as described by Gasol and 165
Del Giorgio, 2000. The empirical calibration between light side scatter (SSC) and cell 166
diameter described by Calvo-Díaz and Morán (2006) were used to estimate the biovolume 167
(BV) of bacterioplankton cells. BV was converted into biomass by using the allometric 168
7
factor of Norland (1993: fg C cell−1 = 120 × BV0.72) for the coastal experiments and using 169
the open ocean conversion factor for the oceanic experiments (fg C cell−1 = 350 × BV). 170
2.4 Nutrients 171
Aliquots for inorganic nutrient determinations (ammonium, nitrite, nitrate, phosphate, 172
and silicate) were collected in first place and directly from the Niskin bottle in order to 173
avoid contamination. Polyethylene bottles 50 ml precleaned with HCl 5 % were filled 174
with the sample employing free-contamination plastic gloves and immediately frozen at 175
–20°C until analysis by standard colorimetric methods with a Bran-Luebbe segmented 176
flow analyzer (Hansen and Grasshoff 1983). The detection limit was 0.1 μmol l−1 for 177
nitrate, 0.02 μmol l−1 for nitrite and phosphate and 0.05 μmol l−1 for ammonium and 178
silicate. Dissolved inorganic nitrogen (DIN) concentration was calculated as the sum of 179
the ammonium, nitrite and nitrate concentrations. 180
2.5 Vitamin B12 181
Seawater samples for dissolved vitamin analysis were taken at surface and SCM depth in 182
the coastal and oceanic station on the first, third and fifth (or sixth) day of each cruise 183
(Table S1 in the Supplement). Samples were filtered through 0.2 μm sterivex filters and 184
frozen at -20ºC until further analysis. Samples (1 l) were preconcentrated using a solid-185
phase extraction with a C18 resin (Bondesil C18, Agilent) at pH 6.5 and rate of 1ml/min. 186
Elution was performed with 12 ml of methanol (MeOH) LCMS grade that was removed 187
via evaporation with nitrogen in a Turbovap. Residual water behind (300-500 µl) was 188
frozen at -20ºC until further analysis using liquid chromatography coupled to mass 189
spectrometry system. 190
The concentrate was filtered again through a cellular acetate membrane 0.2 μm 191
(Phenomenex) prior to the analysis. Ultra Performance Liquid Chromatography tandem 192
Mass Spectometry 3Q (UPLC-MS/MS) methodology was adapted from Sañudo-193
Wilhelmy et al (2012), Heal et al. (2014) and Suffridge et al (2017). Detection and 194
quantification of dissolved vitamin B12 (cyanocobalamin and hydroxocobalamin) was 195
conducted using an Agilent 1290 Infinity LC system (Agilent Technologies, Waghaeusel-196
Wiesental, Germany), coupled to an Agilent G6460A triple quadrupole mass 197
spectrometer equipped with an Agilent Jet Stream ESI source. The LC system used a C18 198
reversed-phase column (Agilent Zorbax SB-C18 Rapid Resolution HT (2.1 × 50 mm, 1.8 199
8
μm) with a 100 μl sample loop. Agilent Technologies software was used for data 200
acquisition and analysis. Chromatographic separation was performed using MeOH and 201
water LCMS grade, both buffered to pH 5 with 0.5 % acetic acid, as mobile phases in a 202
15 minutes’ gradient. Gradient starting at 7 % MeOH for 2 min, changing to 100 % MeOH 203
by minute 11, continuing at 100 % MeOH until 13.5 min and returning to initial 204
conditions to complete 15 min. Limits of detection (LODs) and limits of quantification 205
(LOQs) were determined using sequential dilutions of the lowest point of the calibration 206
curves. LODs were defined as the lowest detectable concentration of the analyte with a 207
signal-to-noise (S/N) ratio for the qualitative transition of at least 3. In the same way, 208
LOQs were defined as the lowest quantificable concentration with a S/N ratio of 10 for 209
the quantitative transition. S/N ratios were calculated using the Mass Hunter Workstation 210
software B.04.01. The LODs obtained for the two vitamin B12 congeners were 0.04 and 211
0.01 pM, while the LOQs values were 0.05 and 0.025 pM for hidroxocobalamin 212
(OHB12). The average B12 recovery percentage after pre-concentration and extraction 213
of B-vitamin spiked samples was 93%. B-vitamin free seawater was spiked with CNB12 214
and OHB12 standards for recovery percentage analysis. 215
2.6 Microbial plankton community 216
DNA samples were taken during the experimental period at surface and SCM depth in 217
the coastal and oceanic station. In particular, sampling of the microbial plankton 218
community was carried out on the first, second, fourth and sixth day of each cruise. 219
Community composition was assessed by sequencing the V4 and V5 regions from 16S 220
rRNA gene (16S rDNA) for prokaryotes and the V4 region from 18S rRNA gene (18S 221
rDNA) for eukaryotes. Two litters of water samples were sequentially filtered through 3 222
µm pore size polycarbonate filters and 0.2 µm pore size sterivex filter and immediately 223
frozen in liquid nitrogen and conserved at -80 ºC. DNA retained in the 3 µm and 0.2 µm 224
filters was extracted by using the PowerSoil DNA isolation kit (MoBio Laboratories 225
Inc., CA, USA) and the PowerWater DNA isolation kit (MoBio Laboratories Inc., 226
CA, USA), respectively, according to the manufacturer’s instructions. Prokaryotic DNA 227
from 0.2 µm filters was amplified using the universal primers “515F and 926R” and 228
eukaryotic DNA from both, 3 µm and 0.2 µm filters, using the primers 229
“TAReuk454FWD1” and “TAReukREV3”. Amplified regions were sequenced in an 230
Illumina MiSeq platform and the sequences obtained were analyzed with software 231
package DADA2 (Callahan et al., 2016). SILVA reference database (Quast et al., 2012) 232
9
was used to taxonomic assignment of 16S amplicon sequence variants (ASVs) and PR2 233
(Guillou et al., 2012) and the marine protist database from the BioMarks project (Massana 234
et al., 2015) were used to taxonomic assignment of 18S ASVs. The data for this study 235
have been deposited in the European Nucleotide Archive (ENA) at EMBL-236
EBI (https://www.ebi.ac.uk/ena) under accession numbers XXXXXX (16S rDNA 237
sequences) and YYYYYY (18S rDNA sequences). ASV table is an analogue of the 238
traditional OTU table which records the number of times each exact amplicon sequence 239
variant was observed in each sample (Callahan et al., 2016). 240
The raw ASV tables of prokaryotes and eukaryotes were subsampled to the number of 241
reads present in the sample with the lowest number of reads, which was 2080 and 1286, 242
for 16S rDNA and 18S rDNA, respectively. The abundance of ASVs was averaged for 243
coastal and oceanic samples, differentiating surface and SCM. A total of 1550 unique 244
ASVs of prokaryotes were identified. As many ASVs of eukaryotes were present in both 245
size fractions, we combined datasets derived from the 0.2 and the 3 μm filters for 246
eukaryotic community analyses. As explained in Hernández-Ruiz et al. (2018), we 247
normalized the reads from each filter size by the filter DNA yield, as recommended in 248
Dupont et al. (2015), obtaining 2293 unique ASVs. The sequence abundances of the 249
subsampled ASV tables were transformed using the centered log ratio (clr) (Fernandes et 250
al., 2014; Gloor et al., 2017). Zeros were replaced by the minimum value that is larger 251
than 0 divided by 2. 252
2.7 Statistical analysis 253
To compare the effect of different nutrient additions on the response variables, 254
phytoplankton and bacterial biomasses, we calculated response ratios (RR) by dividing 255
each observation (mean of triplicates) of each treatment by the respective control 256
treatment mean. A value equal to 1 implies no response, a value < 1 implies a negative 257
response and a value > 1 implies growth stimulation after nutrient addition. Secondary 258
limitation by B vitamins was calculated by dividing the mean biomass value in the 259
inorganic nutrients and B vitamin combined treatment by the mean biomass value in the 260
inorganic nutrient addition treatment. In the same way, a value < 1 implies a negative 261
effect of B vitamins and a value > 1 implies growth stimulation by B vitamin through 262
secondary limitation. 263
10
Normal distribution was tested by a Kolmogorov-Smirnov test and variables were log 264
transformed if necessary to attain normality. All statistical analysis were considered 265
significant at the 0.05 significance level and p-value was standardized as proposed by 266
Good (1982) in order to overcome the low number of replicates. Differences between 267
station and depth (spatial variability) and among sampling months (temporal variability) 268
in the responses to B vitamins were evaluated with factorial analysis of variance 269
(ANOVA). Bonferroni post hoc tests analyses were conducted to test which treatments 270
were significantly different from the control treatment in each experiment. Z-test was 271
used to evaluate the significance of the average B vitamins response ratios for each period, 272
sampling site and depth. The RELATE analysis implemented in PRIMER6 (Clarke and 273
Warwick, 2001; Clarke and Gorley, 2006) was used to relate the B-vitamin response 274
patterns (Bray-Curtis resemblance matrix built from phytoplankton and bacteria response 275
ratios) with: (1) environmental factors (Euclidean resemblance matrix built from 276
normalized values of ammonium, nitrite, nitrate, phosphate, silicate, B12, temperature, 277
salinity, chlorophyll—a, bacterial biomass), (2) prokaryote community composition 278
(Euclidean resemblance matrix built form clr-transformed sequence abundance of major 279
taxonomic groups), or (3) eukaryote community composition (Euclidean resemblance 280
matrix built form clr-transformed sequence abundance of major taxonomic groups). 281
RELATE calculates the Spearman rank correlations (Rho) between two resemblance 282
matrices, and the significance is tested by a permutation test. In order to highlight which 283
specific taxonomic groups are associated to changes of microbial plankton 284
(bacterioplankton and phytoplankton) responses to vitamin B1 and B12, we conducted a 285
distance based redundancy analysis (dbRDA) combined with a distance linear-based 286
model (DistLM) using a step-wise procedure and adjusted r2 as selection criteria) using 287
the PRIMER6 software. Correlations among the prokaryotic taxa best explaining the 288
microbial plankton responses to B-vitamins (according to the previously tests) and 289
phytoplankton and bacterial responses to different B vitamin treatments (including 290
primary and secondary responses) were calculated using Pearson’s correlations. 291
3 Results 292
3.1 Initial conditions 293
Different hydrographic conditions were found during each cruise (Fig. 1 and Fig. 2). In 294
February, heavy rainfall combined with relaxed winds (Fig. 1) caused a halocline at 10 295
11
meters depth (Fig. 2). High levels of Chl-a (as derived from the calibrated CTD 296
fluorescence sensor) were observed at the coastal station, being maximum (4.97 µg l-1) 297
by the end of the cruise. At the oceanic station, Chl-a levels remained low (less than 3 µg 298
l-1) throughout the cruise, being slightly higher in the subsurface layer. 299
Strong precipitation during the April cruise (Fig. 1) caused a persistent surface halocline 300
at the coastal station (Fig. 2). Maximum Chl-a concentrations ranged from 0.99 to 2.73 301
µg l-1, declining from day 5 onwards, coinciding with an increase in water temperature 302
associated to a downwelling situation. At the oceanic station, a persistent subsurface Chl-303
a maximum (up to 1.61 µg l-1) was observed throughout the cruise. 304
In August, strong thermal stratification was observed at both stations (Fig. 2). At the 305
beginning of the cruise, high Chl-a concentration (close to 20 µg l-1) was observed in 306
subsurface water. These high Chl-a levels were maintained until day 4 and then 307
decreased, reaching minimum values by day 7, coinciding with upwelling relaxation (Fig. 308
1b and Fig. 2). Salinity minima during day 1 and 5 reflect precipitation events. Chl-a was 309
relatively low at the oceanic station, an increased by the end of the sampling period as a 310
consequence of an upwelling event, that brought cold and nutrient rich water to the 311
surface, at day 5 (Fig. 2). 312
Abiotic and biotic conditions at the beginning of each experiment are shown in Fig. 3 and 313
in the supplementary Table S2. Overall, the concentration of dissolved inorganic nitrogen 314
(DIN) was higher at the coastal than at the oceanic station, where very low levels were 315
measured in August (Fig. 3). At the coastal station, higher DIN concentrations were 316
observed in surface compared to subsurface waters. The DIN:DIP (dissolved inorganic 317
phosphorous) ratio was always lower in open ocean than in the coastal station and mostly 318
below of Redfield ratio. Phosphorous limitation (DIN:DIP > 16) was frequent in coastal 319
subsurface waters in February and April. 320
Phytoplankton biomass, estimated as Chl-a concentration greatly varied between stations 321
and seasons but was always higher at the coastal (st3) than at the oceanic (st6) station 322
(Fig. 3). Bacterial biomass (BB) increased from winter (February cruise) to summer 323
(August cruise) at the two stations. In February, Chl-a concentrations increased by the 324
end of the cruise at both coastal and oceanic stations, while bacterial biomass remained 325
very low throughout this sampling period. In April, both BB and Chl-a were similar in 326
the ocean and the coast, and showed reduced temporal variability, irrespective of the 327
12
observed nutrient variability (Fig. 3). In August, Chl-a concentration was much higher at 328
the coastal than at the oceanic station, and showed reduced temporal variability (except 329
at the SCM in the coast) (Fig. 3). At the beginning of the sampling period, BB was higher 330
in the ocean than in the coast, and tended to decline by the end of the cruise. 331
A MDS analysis revealed that microbial community composition showed a relatively 332
reduced within period variability, with samples clustering according to the sampling 333
period (ANOSIM, p = 0.001) (Fig. S1 in the Supplement). Consequently, we averaged 334
the microbial community composition for each period and sampling site. The sampling 335
period-averaged composition of the eukaryote community showed a clear variability 336
among sampling dates, while differences between sampling locations and depths were 337
less pronounced (Fig. 4a). At the coastal location, Mamiellophyceae were relatively 338
abundant in February and April, but their abundance sharply decreased in August. By 339
contrast, the relative abundance of Dinophyceae was highest in August at both sampling 340
locations. The contribution of diatoms (Bacillariophyta) was very low in summer at the 341
oceanic station and MALV were most representative in February at both locations. 342
Flavobacterales and Rhodobacterales were the dominant prokaryotes (Fig. 4b) in coastal 343
waters, particularly in August, when both represented more than 80 % of sequences, while 344
Cyanobacteria were mostly present in February and April. In oceanic waters, 345
Flavobacterales and Cyanobacteria were the dominant prokaryotes. SAR11 clade and 346
Archaea were most abundant in February at both sampling locations. 347
B12 concentration was low, ranging from 0.06 to 0.66 pM (Table S1 in the Supplement) 348
Mean B12 concentration was significantly higher in the coast (0.30±0.13 pM) than in the 349
ocean (0.15±0.12 pM) (t-test, p = 0.001), and showed less variability at the coastal than 350
at the oceanic station (Fig. 4c). 351
3.2 Short-term phytoplankton and bacteria responses to inorganic nutrients and 352
vitamin additions 353
The temporal evolution of the phytoplankton and bacterial biomass in the control 354
treatments showed different patterns. Phytoplankton biomass remained either stable or 355
increased after 72 h of incubation in most of the experiments conducted in February and 356
April. However, phytoplankton biomass mostly decreased in the coastal experiments 357
conducted in August (Fig. 5). A very similar pattern was observed for bacterial biomass, 358
13
although the decrease in biomass occurred both in the coastal and in the oceanic stations 359
during summer (Fig. 6). 360
The magnitude of phytoplankton and bacteria responses (i.e., the response ratios) to the 361
different addition treatments differed between sampling stations (ANOVA, p = 0.018) 362
and among sampling periods (ANOVA, p = 0.014). The most prominent responses of 363
phytoplankton, compared to the control treatment, occurred after inorganic nutrient 364
amendments, especially in surface oceanic waters (Fig. 5 and Fig. S2 in the Supplement). 365
The magnitude of the phytoplankton response to inorganic nutrients was significantly 366
higher in oceanic than in coastal waters (ANOVA, p = 0.028). Bacteria responded 367
comparatively less than phytoplankton to inorganic nutrients (Fig. 6) and there were no 368
significant differences between coastal and oceanic waters (ANOVA, p = 0.203). The 369
addition of inorganic nutrients caused significant increases in phytoplankton biomass in 370
31 out of the 36 experiments, and in 19 out of 36 experiments in bacterial biomass (Fig 371
5, Fig. 6 and Fig. S2 in the Supplement). 372
The addition of B12 stimulated phytoplankton growth in 5 out of 36 experiments (Fig. 5 373
and Fig. S3 in the Supplement) while bacteria responded positively to B12 in 6 374
experiments (Fig. 6 and Fig. S3 in the Supplement). Phytoplankton biomass increased in 375
3, and bacterial biomass in 7 out of 36 experiments after adding B1 (Fig. 5 and Fig. 6). B 376
vitamins also caused negative responses of phytoplankton (Fig. 5 and Fig. S3 in the 377
Supplement) and bacterial biomass (Fig. 6 and Fig. S3 in the Supplement). The addition 378
of vitamins induced decreases of phytoplankton biomass in 6 experiments (4 after adding 379
B12 and 2 after adding B1) and bacterial biomass in 14 experiments (6 after adding B12 380
and 8 after adding B1). Additions of inorganic nutrients combined with B-vitamins 381
caused a similar increase in phytoplankton or bacterial biomass than the inorganic 382
addition alone in most of the experiments. Secondary limitation by B1 and/or B12 was 383
occasionally observed when inorganic nutrients were limiting, leading to a higher 384
biomass increase in the treatments including both inorganic nutrients and vitamins as 385
compared to the inorganic nutrient addition alone (Fig. 5, Fig. 6 and Fig. S3 in the 386
Supplement). In the case of phytoplankton, secondary limitation by B-vitamins was found 387
in the 3b-surface, 6a-SCM and 6b-SCM experiments in February, in the 3b-surface and 388
3b-SCM experiments in April, and in the 3b-SCM, 6b-SCM and 6c-surface experiments 389
in August (Fig. 5). 390
14
In order to quantify the relevance of inter-day variability, we calculated the mean 391
coefficient of variation (CV) of the responses to B vitamins (i.e., excluding the responses 392
to inorganic nutrients, and normalizing the responses of the nutrient and vitamin 393
combined treatments to the corresponding response to inorganic nutrients alone) within 394
sampling periods for each sampling point (4 sites during 3 periods). The CV ranged from 395
9%, in subsurface oceanic waters in April, to 34% in surface coastal waters in April, 396
averaging 16±6 (SD) % (data not shown). Considering that short-term (within sampling 397
period) variability was overall very low, and for simplicity, we averaged the responses to 398
B vitamins in the 3 experiments conducted at each of the 12 sampling points to further 399
describe spatial and temporal patterns in the response to B vitamin amendments (Fig. 7). 400
3.3 B-vitamin response patterns in relation to environmental factors and prokaryote 401
and eukaryote community composition 402
When averaging the responses within each sampling point (Fig. 7), some general patterns 403
emerge. Both phytoplankton and bacteria showed more negative than positive responses 404
to B1 and/or B12 amendments. Most positive responses occurred at the oceanic station, 405
while negative responses dominated in the coast. Phytoplankton significant positive 406
responses mostly occurred in February, showing an average increase of up to 1.2-fold in 407
coastal subsurface waters after B12+B1 amendment (Fig. 7). The largest significant 408
increase in phytoplankton biomass (ca. 1.4-fold) occurred in April after the combined 409
addition of B12 and B1 in coastal surface waters. Significant positive bacterial responses 410
mainly occurred in August, when the largest increase (ca. 1.3-fold) occurred in coastal 411
subsurface waters after B1 amendment (Fig. 7). Most positive responses were associated 412
with treatments containing B12 either alone or combined with B1 (Fig. 7). Phytoplankton 413
primary B1 limitation was only found at the oceanic SCM in February (Fig. 7), while 414
bacterial primary B1 limitation only occurred at the coastal SCM in August. In addition, 415
bacterial secondary B1 limitation occurred in oceanic surface waters in February and 416
August. 417
In order to explore the controlling factors of the observed B-vitamin response patterns, 418
the correlation between the B-vitamin response resemblance matrix and the 419
corresponding resemblance matrices obtained from the initial environmental factors, the 420
initial prokaryotic community composition, or the initial eukaryotic community 421
composition were calculated. Only the prokaryotic community composition significantly 422
15
correlated with the B-vitamin responses (Spearman Rho = 0.31, p = 0.041). We then used 423
distance-based linear modelling (DistLM) to identify the prokaryotic taxa which best 424
explained the microbial plankton responses to B-vitamins (Fig. 8). The resulting model 425
explained 78 % of the variation and included seven prokaryotic groups: Planktomarina, 426
Actinobacteria, SAR11_clade, Cellvibrionales, Euryarchaeota, Flavobacteriales and 427
Synechococcus. The sequential test identified Planktomarina and Actinobacteria as the 428
taxa explaining the largest fraction of variation (ca. 24 % and 14%, respectively, data not 429
shown). The total variation explained by the db-RDA1 and db-RDA2 was 59.4 %, both 430
represented as x and y axis, respectively (Fig. 8). The db-RDA1 axis tended to separate 431
coastal, where negative responses to B vitamins dominated, from oceanic samples, where 432
most positive responses were found (Fig. 7). The db-RDA plot showed that 433
Cellvibrionales and Plankomarina highly and positively correlated with axis 1, while 434
SAR11 and Synechococcus showed negative correlation with axis 1. Flavobacteriales and 435
Actinobacteria mostly correlated with the db-RDA2 axis. 436
4 Discussion 437
Although the dependence of phytoplankton on B vitamin has been previously observed 438
in cultures (e.g. Croft et al., 2006; Droop, 2007; Tang et al., 2010) and in natural microbial 439
assemblages in coastal areas (e.g. Sañudo-Wilhelmy et al., 2006; Gobler et al., 2007; 440
Koch et al., 2011, 2012, Barber-Lluch et al., 2019), this is, to the best of our knowledge, 441
the most complete study about responses of phytoplankton and bacterial biomass to 442
vitamin B12 and/or B1 addition. The 36 experiments developed in this study allowed a 443
detailed evaluation of the role of vitamins B12 and B1 at different spatial and temporal 444
scales. 445
Contrary to our expectations, inter-day variability of microbial responses to B vitamins 446
and microbial plankton community composition was relatively small (Fig. 5, Fig. 6, and 447
Fig. S1 in the supplement). The reduced short-term variability in the responses to B 448
vitamins additions suggested that B vitamin availability might be controlled by factors 449
operating at larger temporal scales, such as the succession of microbial communities 450
associated to seasonal environmental variation (Hernández-Ruiz et al., 2018; Hernando-451
Morales et al., 2018). Considering this, and for further discussion, we averaged the 452
responses from the three experiments conducted during each sampling period, resulting 453
in a total of 12 experimental situations (2 stations × 2 depths × 3 periods). Overall, 454
16
phytoplankton and/or bacterial growth enhancement in at least one B vitamin treatment 455
was frequent but relatively moderate in this productive ecosystem, showing 1.1 to 2.4-456
fold increases in 75% of the experimental situations for phytoplankton and in 50% for 457
bacteria. On the other hand, negative responses to at least one B vitamin treatment 458
occurred in all but one of the experimental situations (Fig. 7). The low and constant B12 459
ambient concentration (Fig. 4) and the reduced magnitude of microbial responses suggest 460
a close balance between production and consumption of this growth factor. Different 461
patterns of response to B-vitamin amendments were observed in phytoplankton and 462
bacteria, which appear to be mostly explained by the prokaryotic community 463
composition. 464
4.1 Positive responses to vitamin B1 and B12 amendments 465
The experimental design allowed the detection of two categories of B vitamin dependency 466
of the microbial plankton community. A primary limitation by B vitamins occurs when 467
microorganisms respond to additions of B vitamins alone, while a secondary limitation 468
by B vitamins arises when the response to the combined addition of B vitamins and 469
inorganic nutrients is significantly higher than that to inorganic nutrients alone, as a result 470
of the ambient B-vitamin depletion associated to the plankton growth after inorganic 471
nutrient enrichment. Most positive (72% for phytoplankton and 60 % for bacteria) 472
responses occurred after single B-vitamins additions, suggesting that inorganic nutrient 473
availability enhance B-vitamin production by the prototrophic microbes. Under nutrient-474
limiting conditions, the external supply of vitamins could reduce the energy costs 475
associated to its synthesis (Jaehme and Slotboom, 2015), stimulating the growth not only 476
of auxotrophs but also of prototrophs. 477
The significant positive effects of B12 and/or B1 addition, suggest that these compounds 478
may be eventually limiting microbial growth in marine productive ecosystems, as 479
previously observed by other authors (e.g., Panzeca et al., 2006; Sañudo-Wilhelmy et al., 480
2006; Bertrand et al., 2007; Gobler et al., 2007; Koch et al 2011; 2012; Barber.-Lluch et 481
al 2019 ). Most positive responses to B vitamin amendments were observed in oceanic 482
waters, where B12 concentration was significantly lower than in coastal waters (Fig. 4c). 483
Unfortunately we lack B1 measurements in this study, but, according to previous field 484
studies in other oceanographic regions, a similar pattern to that observed for B12 can be 485
expected (Cohen et al., 2017; Sañudo-Wilhelmy et al., 2012; Suffridge et al., 2018). The 486
17
overall low and stable concentration of B12 at both sampling locations suggests a high 487
turnover time of this compound in these productive, well-lit waters. Rapid cycling of B12 488
in surface waters may occur due to high biological uptake rates (Taylor and Sullivan, 489
2008; Koch et al., 2012) and/or photochemical degradation (Carlucci et al., 1969; 490
Juzeniene and Nizauskaite, 2013; Juzeniene et al., 2015). The measured B12 491
concentrations were in the lower range reported for coastal sites, and similar to that found 492
in the upwelling system off the California coast in the San Pedro Basin during winter, 493
spring and summer (Panzeca et al., 2009). 494
The increase of phytoplankton biomass was mostly associated to B12 amendments, which 495
is consistent with the known incapability of eukaryotes to synthesize this vitamin (Croft 496
et al., 2005; Tang et al., 2010; Sañudo-Wilhelmy et al., 2014). Considering the very low 497
concentration of B12 in the sampling area, the relatively limited phytoplankton response 498
to B vitamins is consistent with the presence of species that may have adapted to 499
overcome B12 limitation in the environment by using alternative enzymes. For example, 500
changes in external B12 availability may cause shifts from vitamin B12-dependence to 501
vitamin B12-independence in taxa possessing the vitamin B12-independent methionine 502
synthase (MetE) gene (Bertrand et al., 2013; Helliwell et al., 2014). Other strategies used 503
by phytoplankton to cope with low cobalamin concentration include, increased cobalamin 504
acquisition machinery, decreased cobalamin demand, and management of reduced 505
methionine synthase activity through changes in folate and S-adenosyl methionine 506
metabolism (Bertrand et al., 2012). The available data on B12 half-saturation constants 507
for phytoplankton (0.1-10 pM) (Droop, 1968, 2007; Taylor and Sullivan, 2008; Tang et 508
al., 2010; Koch et al., 2011) are similar or higher than the B12 concentrations measured 509
here (0.3 pM in the coastal and 0.15 pM in the oceanic waters, on average), reinforcing 510
the hypothesis of a phytoplankton community adapted to B12 limiting concentrations in 511
this upwelling system. 512
The positive responses of phytoplankton in surface oceanic waters in February seemed to 513
be associated with high abundance of Synechococcus and SAR11 (Fig. 4a and Fig. 8). 514
Synechococcus produce a B12 analog known as pseudocobalamin, where the lower ligand 515
base adenine replaces 5,6-dimethylbenzimidazole (DMB) (Helliwell et al., 2016). In 516
natural conditions, pseudocobalamin is considerably less bioavailable to eukaryotic algae 517
than other cobalamin forms (Helliwell et al., 2016; Heal et al., 2017). SAR11 do not 518
require B12 and do not have pathways for its synthesis (Sañudo-Wilhelmy et al., 2014; 519
18
Gómez-Consarnau et al., 2018), suggesting that B12 synthesis could be limited in oceanic 520
waters in winter, due to the low abundance of potentially B12 producers. 521
Microbial responses to B vitamins in subsurface oceanic waters in February were 522
associated to high abundance of Synechococcus and, to some extent, of Actinobacteria 523
(Fig. 8). In these experiments, positive effects of B1 addition on phytoplankton and 524
bacteria were observed (Fig. 7). While Synechococcus is capable of B1 synthesis (Carini 525
et al., 2014; Sañudo-Wilhelmy et al., 2014; Gómez-Consarnau et al., 2018), 526
Actinobacteria seems to have a strong dependence on this vitamin (Gómez-Consarnau et 527
al., 2018). Among the sequenced eukaryote genomes, only Stramenopiles contain genes 528
codifying for the synthesis of thiamine monophosphate (Sañudo-Wilhelmy et al., 2014; 529
Cohen et al., 2017) . While Stramenopiles, dominated by Bacillariophyta, were ubiquitous 530
in the sampling area, their relative contribution was lower in oceanic waters (Fig. 4). The 531
simultaneous stimulation of phytoplankton and bacteria by B1 addition in subsurface 532
oceanic waters in winter suggest a strong demand for this compound under these 533
particular conditions, however what triggers the observed responses remain unclear. 534
Even though B1 caused a significant effect on phytoplankton only in subsurface waters 535
in winter, half of the positive responses of bacteria were associated to B1 supply (Fig. 7). 536
This pattern is consistent with the recently described widespread dependence of 537
bacterioplankton on external B1 supply (Paerl et al., 2018). B1 stimulated bacterial 538
growth in subsurface coastal waters and surface oceanic waters in summer (Fig. 7), when 539
the B vitamin response patterns were associated to high abundance of Planktomarina and 540
Actinobacteria (Fig. 8),which are expected to strongly depend on external B1 sources 541
(Giebel et al., 2013; Gómez-Consarnau et al., 2018). The generalized significant and 542
positive bacterial responses to vitamin treatments in surface oceanic waters in summer, 543
when the bacterial biomass was high and dissolved inorganic nitrogen concentration was 544
very low (Fig. 3), suggest that bacteria may have an advantage in the uptake and 545
assimilation of B vitamins under nitrogen limiting conditions. 546
4.2 Negative responses to vitamin B1 and B12 amendments 547
Similar experiments conducted in this area also reported negative responses of microbial 548
plankton to vitamin B12 additions (Barber-Lluch et al., 2019). The predominantly 549
negative bacterial responses after vitamin amendments in the coast during summer (Fig. 550
6, Fig. 7, and Fig. S3 in the Supplement), when nutrient concentrations were low (Fig. 3), 551
19
suggest either a strong competition between phytoplankton and bacteria or a stimulation 552
of predation. Dinoflagellates were particularly abundant in summer at both sampling sites 553
and depths. Many dinoflagellate species are auxotrophs for B1 and/or B12 (Croft et al, 554
2006; Tang et al., 2010), and also many of them are phagotrophs (Stoecker and Capuzzo, 555
1990; Smayda, 1997; Sarjeant and Taylor, 2006; Stoecker et al., 2017), thus the external 556
supply of B vitamins may have promoted their growth, ultimately leading to net decreases 557
in microbial biomass at the end of the experiments. Several studies demonstrated that 558
vitamin B12 is implicated in the occurrence of dinoflagellate blooms around the world 559
(Aldrich, 1962; Carlucci and Bowes, 1970; Takahashi and Fukazawa, 1982; Yu and 560
Rong-cheng, 2000). It has been suggested that the B12-dependent enzyme 561
methylmalonyl-CoA mutase in dinoflagellate, euglenoid, and heterokont algae allows 562
them to grow heterotrophically when B12 is available (Croft et al., 2006). Therefore, the 563
B12 enrichment could trigger such nutritional strategy, particularly in summer, when 564
mineral nutrients are less available, resulting in an increased predation pressure on 565
bacteria. 566
Strikingly, the B vitamin response patterns in surface coastal waters in summer (Fig. 7), 567
seemed to be associated with high abundance of Flavobacteriales (Fig. 8). All isolates of 568
Bacteroidetes sequenced so far are predicted to be B12 auxotrophs (Sañudo-Wilhelmy et 569
al., 2014; Gómez-Consarnau et al., 2018) and recent metatranscriptomic analyses revel 570
that B1 synthesis gene transcripts are relatively low in Flavobacteriia as a group (Gómez-571
Consarnau et al., 2018). As both phytoplankton and bacteria are dominated by potentially 572
B12 and B1 auxotrophs (dinoflagellates and Flavobacteriales) in the coast during summer 573
(Fig. 4), the negative responses could be the result of strong competition for B vitamins. 574
However, the negative responses to B vitamins of both phytoplankton and bacteria in 575
surface coastal water in summer suggests an increase in predation over both microbial 576
groups rather than competition between them. By contrast, bacteria and phytoplankton 577
showed opposite patterns of response to B vitamins in subsurface coastal waters in 578
summer, which suggests competition between both microbial compartments (Fig. 7). 579
While phytoplankton negatively responded only to single B vitamin additions, bacteria 580
responded negatively only when both inorganic nutrients and B vitamins were added (Fig. 581
7). It is conceivable that phytoplankton had an advantage over bacteria when mineral 582
nutrients were added. 583
5 Conclusions 584
20
In conclusion, our findings suggest that the heterogeneous responses of microbial 585
plankton to B1 and B12 vitamins supply in this coastal upwelling system could be 586
partially controlled by the composition of the prokaryote community, which is consistent 587
with their major role as B12 producers and B1 consumers. The overall moderate 588
responses in terms of biomass together with the low ambient B12 concentration, suggest 589
that the microbial plankton in this area is well adapted to cope with B vitamin shortage 590
and that a close balance exists between production and consumption of these important 591
growth factors. 592
593
Author contribution. 594
Eva Teira designed the experiments and Vanessa Joglar carried them out with 595
contributions from all co-authors. Vanessa Joglar analyzed the data, Vanessa and Eva 596
Teira interpreted the results and Vanessa Joglar prepared the manuscript under Eva Teira 597
supervision. 598
Competing interests. The authors declare that they have no conflict of interest. 599
Acknowledgements 600
We thank all the people involved in the project ENVISION for helping with sampling 601
and analytical work. We also thank the crew of the Ramón Margalef for their help during 602
the work at sea. From IIM-CSIC, V. Vieitez and M.J. Pazó performed the nutrient 603
analyses. This research was supported by the Spanish Ministry of Economy and 604
Competitiveness through the ENVISION project (CTM2014-59031-P). Vanessa Joglar 605
was supported by a FPI fellowship from the Spanish Ministry of Economy and 606
Competitiveness. 607
608
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of adenosylcobalamin (vitamin B12), Nat. Prod. Rep., 19, 390–412, 920
doi:10.1039/b108967f, 2002. 921
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red tide occurrence, Chinese J. Oceanol. Limnol., 18, 253–259, 924
doi:10.1007/BF02842672, 2000. 925
926
31
7 Figures 927
Figure 1: (a) The NW Iberian margin (rectangle) and locations of the stations that were 928
sampled in the Ría de Vigo (st3) and on the shelf (st6) (diamonds), (b) distribution of 929
daily coastal upwelling index (Iw) and (c) registered precipitations during each sampling 930
period showing the initial time of each experiment (3a, 3b, 3c and 6a, 6b, 6c). ns: no 931
sampling day. 932
Figure 2: Vertical distribution in the coastal station of (a) fluorescence (µg l-1), (b) 933
temperature (ºC) and (c) salinity (PSU) over time for February, April and August and 934
vertical distribution in the oceanic station of (d) fluorescence (µg l-1), (e) temperature (ºC) 935
and (f) salinity (PSU) over time for February, April and August. 936
Figure 3: Initial biological conditions and abiotic factors at the coastal (st3) and oceanic 937
(st6) sampling stations. Each bar corresponds to one of the 3 experiments performed in 938
each depth and station during February, April and August. (a), Chl-a, total Chl-a (µg l-1); 939
(b) BB, bacterial biomass (µg C l-1); (c) DIN, dissolved inorganic nitrogen (µmol N l-1)940
and (d) DIN:DIP, ratio nitrogen:phosphate. 941
Figure 4: (a) Averaged relative contribution of reads to the major taxonomic groups of 942
eukaryotes and prokaryotes at surface and SCM in the coastal and oceanic station in 943
February, April and August. (b) Averaged B12 concentration (pM) at surface and SCM 944
in the coastal and oceanic station in February, April and August. 945
Figure 5: Phytoplankton biomass (estimated as Chl-a concentration) (µg l-1) in the time-946
zero of each experiment (striped bars) and in the final-time of each treatment (colored 947
bars) in the experiments conducted at surface and SCM in the coastal and oceanic station 948
in February, April and August. 949
Figure 6: Bacterial biomass (µgC l-1) in the time-zero of each experiment (striped bars) 950
and in the final-time of each treatment (colored bars) in the experiments conducted at 951
surface and SCM in the coastal and oceanic station in February, April and August. 952
Figure 7: Monthly averaged response ratio (RR) of (a) total phytoplankton community 953
and of (b) bacterial community at surface and SCM in the coastal and oceanic station. 954
Horizontal line represents a response equal to 1, that means no change relative to control 955
in the pink bars (treatments with vitamins alone) and no change relative to inorganic (I) 956
treatment in the green bars (vitamins combined with I treatments). Asterisks indicate 957
phytoplankton or bacterial significant response relative to control or I (Z-test; * p < 0.05) 958
and a indicate response with a level of significance between 0.05 and 0.1 (Z-test; a p = 959
0.05-0.06). 960
32
Figure 8: Distance based redundancy analysis (dbRDA) of B vitamin responses by 961
microbial plankton based on Bray-Curtis similarity. Filled and open symbols represent 962
samples from coastal and oceanic station, respectively, numbers correspond to the 963
sampling station, triangles and circles represent samples from surface and SCM, 964
respectively, and colours correspond to the months: (green) February, (blue) April and 965
(pink) August. Only prokaryotic taxa that explained variability in the B vitamin responses 966
structure selected in the DistLM model (step-wise procedure with adjusted R2 criterion) 967
were fitted to the ordination. 968
969
−2000
0
2000
4000
6000
8000
10000
12000
Day −1 0 1 2 3 4 5 6 7 8
UI [
m3
s k
m]
5
10
15
20
25
Prec
ipita
tion
[m
2 ]
fig. 01
Day −1 0 1 2 3 4 5 6 7 8
aa
bb
b
cc
dd
d
ee
e fc
ff
a
fig. 02
Chla (
)
st3 st6 st3 st6 st3 st6
0
0
10
20
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40
0
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10
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30
Station
epth
0mSCM
Initial Nutrients and Bioma ses
fig. 03
0m SCM 0m CM
0m SCM 0m CM
fig. 04
Surface Oceanic station
5
10
40
SCM Oceanic station
0
5
10
15
20
3T0 C I B12 B1 B12+B1 I+B12 I+B1 I+B12+B1
C IT0 B12 B1
B12+B1
I+B12
I+B1
I+B12+B1 C IT0 B12 B1
B12+B1
I+B12
I+B1
I+B12+B1 C IT0 B12 B1
B12+B1
I+B12
I+B1
I+B12+B1 C IT0 B12 B1
B12+B1
I+B12
I+B1
I+B12+B1 C IT0 B12 B1
B12+B1
I+B12
I+B1
I+B12+B1 C IT0 B12 B1
B12+B1
I+B12
I+B1
I+B12+B1 C IT0 B12 B1
B12+B1
I+B12
I+B1
I+B12+B1 C IT0 B12 B1
B12+B1
I+B12
I+B1
I+B12+B1 C IT0 B12 B1
B12+B1
I+B12
I+B1
I+B12+B1
Surface Coastal station
Chl
-a (
)
0
2
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8
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SCM Coastal station
3a 3b 3c 3a 3b 3c 3a 3b 3c0
5
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30
5
a b c a b c a b c
fig. 0
Chl
-a (
)C
hl-a
()
Chl
-a (
)
Surface Coastal station
0
5
10
15
20
SCM Coastal station
3a 3b 3c 3a 3b 3c 3a 3b 3c0
5
10
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20
T0 C I B12 B1 B12+B1 I+B12 I+B1 I+B12+B1
Surface Oceanic station
0
5
10
15
20
25
3040
SCM Oceanic station
0
5
10
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20
C IT0 B12 B1
B12+B1
I+B12
I+B1
I+B12+B1 C IT0 B12 B1
B12+B1
I+B12
I+B1
I+B12+B1 C IT0 B12 B1
B12+B1
I+B12
I+B1
I+B12+B1 C IT0 B12 B1
B12+B1
I+B12
I+B1
I+B12+B1 C IT0 B12 B1
B12+B1
I+B12
I+B1
I+B12+B1 C IT0 B12 B1
B12+B1
I+B12
I+B1
I+B12+B1 C IT0 B12 B1
B12+B1
I+B12
I+B1
I+B12+B1 C IT0 B12 B1
B12+B1
I+B12
I+B1
I+B12+B1 C IT0 B12 B1
B12+B1
I+B12
I+B1
I+B12+B1
a b c a b c a b c
()
()
()
()
fig. 06
Surface
February April August February April August
0
1
2
3
0
0.5
1.0RR
Phy
topl
ankt
on
B12B1B12+B1I+B12/II+B1/II+B12+B1/I
February April August February April August
0
0.5
1.0
0
0.5
1.0
1.5
RR
Bac
teria
fig. 07
Planktomarina
Synechococcus
fig. 0
1
Table S1: concentration of hydroxocobalamin (OHB12) and cyanocobalamin (CNB12) 1
in seawater samples corresponding to the initial time of the experiments. Abbreviations: 2
Not detected (nd) and lower concentration of the quantification limit (<LOQ). 3
Sample ID Station Depth Month OHB12 CNB 2 pM
1602_st3_d1_p1 coast surface February 0.21 nd 1602_st3_d3_p1 coast surface February 0.20 nd1602_st3_d5_p1 coast surface February 0.26 nd1604_st3_d1_p1 coast surface April 0.47 nd 1604_st3_d3_p1 coast surface April 0.66 nd 1604_st3_d5_p1 coast surface April 0.23 nd 1608_st3_d1_p1 coast surface August 0.30 nd 1608_st3_d3_p1 coast surface August 0.38 nd 1608_st3_d5_p1 coast surface August 0.19 nd 1602_st3_d1_p2 coast SCM February 0.36 nd 1602_st3_d3_p2 coast SCM February 0.10 nd 1602_st3_d5_p2 coast SCM February 0.41 nd 1604_st3_d1_p2 coast SCM April 0.32 nd 1604_st3_d3_p2 coast SCM April 0.27 nd 1604_st3_d5_p3 coast SCM April 0.15 nd 1608_st3_d1_p2 coast SCM August 0.46 nd 1608_st3_d3_p2 coast SCM August 0.21 nd 1608_st3_d5_p2 coast SCM August 0.39 nd 1602_st6_d1_p1 ocean surface February 0.31 nd 1602_st6_d3_p1 ocean surface February 0.09 nd 1602_st6_d5_p1 ocean surface February 0.06 nd 1604_st6_d1_p1 ocean surface April 0.13 nd 1604_st6_d3_p1 ocean surface April 0.09 nd 1604_st6_d6_p1 ocean surface April 0.04 nd 1608_st6_d1_p1 ocean surface August 0.20 nd 1608_st6_d3_p1 ocean surface August 0.09 nd 1608_st6_d6_p1 ocean surface August 0.14 nd 1602_st6_d1_p ocean SCM February 0.21 0. 1602_st6_d3_p2 ocean SCM February 0.08 nd 1604_st6_d1_p2 ocean SCM April nd nd 1604_st6_d3_p2 ocean SCM April 0.07 nd 1604_st6_d6_p2 ocean SCM April 0.05 nd 1608_st6_d1_p2 ocean SCM August 0.19 nd 1608_st6_d3_p2 ocean SCM August 0.09 nd 1608_st6_d6_p2 ocean SCM August 0.16 nd
2
8 Table S2: Summary of initial conditions for each experiment (expt). Sampling months
9 were February (Feb), April (Apr) and August (Aug).
10
11
Station Depth Month Expt Temp Sal NO3- NO2
- NH4+ HPO4
2- DIN:P SiO42- Chl-a BB
ºC μM μM μM μM μM μ g l-1 μgC l-1 Coast surface Feb 3a 13.75 35.02 2.86 0.19 0.35 0.17 19.65 3.62 1.39 1.84
3b 13.22 34.27 4.89 0.36 0.51 0.33 17.25 6.77 0.73 1.91 3c 13.43 34.21 4.63 0.19 0.09 0.18 27.68 8.57 4.86 3.45
Apr 3a 12.96 34.58 2.21 0.24 0.32 0.19 14.55 5.24 2.73 7.88 3b 13.31 34.25 12.46 0.36 0.54 0.41 32.73 12.57 1.40 9.17 3c 14.04 31.83 4.18 0.16 0.55 0.19 25.90 10.52 2.18 4.30
Aug 3a 14.14 35.60 0.50 0.10 0.84 0.12 11.77 1.11 5.73 14.64 3b 14.36 35.61 0.81 0.08 1.08 0.20 9.95 0.28 5.52 6.39 3c 13.66 35.16 3.93 0.17 0.12 0.33 12.78 3.86 5.64 10.61
SCM Feb 3a 13.73 35.71 3.58 0.14 0.04 0.31 12.13 5.25 0.21 1.30 3b 13.91 35.27 4.16 0.15 0.07 0.37 11.91 4.63 0.99 1.83 3c 13.45 34.66 2.94 0.09 0.10 0.17 18.37 6.13 4.98 2.36
Apr 3a 12.80 35.34 3.22 0.34 0.46 0.28 14.34 4.39 0.99 5.90 3b 13.22 35.28 0.24 0.07 0.12 0.04 10.19 2.83 2.15 9.47 3c 13.92 34.95 0.21 0.07 0.10 0.06 6.52 3.41 2.18 9.51
Aug 3a 13.58 35.62 0.91 0.13 0.23 0.15 8.32 1.68 20.75 12.71 3b 13.82 35.61 1.40 0.16 0.14 0.23 7.49 1.40 20.07 1.73 3c 13.38 35.63 5.29 0.13 0.14 0.41 13.47 3.93 4.63 9.21
Ocean surface Feb 6a 13.98 30.20 1.32 0.18 0.11 0.16 10.07 3.23 0.82 2.38 6b 14.16 35.86 0.90 0.11 0.04 0.12 9.15 2.29 1.20 2.98 6c 14.10 35.40 1.03 0.15 0.13 0.16 8.43 2.97 2.08 2.92
Apr 6a 13.44 35.68 0.95 0.11 0.06 0.12 9.63 2.31 1.51 6.58 6b 13.59 35.66 0.47 0.11 0.06 0.08 8.33 2.71 1.29 7.37 6c 13.93 35.57 0.12 0.03 0.06 0.04 4.90 2.08 0.75 11.76
Aug 6a 15.97 35.61 0.05 0.01 0.06 0.02 4.88 1.46 0.65 39.38 6b 16.04 35.59 0.26 0.01 0.09 0.05 7.46 3.21 0.99 11.46 6c 15.34 35.53 0.45 0.04 0.05 0.07 7.38 1.37 1.30 5.63
SCM Feb 6a 14.08 35.75 1.73 0.20 0.04 0.18 11.18 3.47 0.88 2.28 6b 14.10 35.76 1.60 0.19 0.02 0.15 11.75 2.86 1.22 3.18 6c 14.13 35.82 1.13 0.18 0.12 0.16 9.17 2.92 2.39 3.49
Apr 6a 13.28 35.69 1.63 0.31 0.10 0.18 11.51 3.16 1.61 5.38 6b 13.28 35.68 1.45 0.33 0.12 0.16 11.88 2.42 1.50 6.96 6c 13.72 35.60 0.03 0.06 0.07 0.05 3.01 1.89 1.45 11.74
Aug 6a 14.90 35.60 0.00 0.04 0.10 0.03 4.20 1.44 0.84 26.55 6b 15.95 35.60 0.27 0.00 0.07 0.05 6.45 2.79 1.11 6.04 6c 15.41 35.62 0.35 0.06 0.06 0.07 6.51 1.66 1.41 5.45
3
Figure S1: A multidimensional scaling (MDS) showing the distance according to 12
similarity in the microbial plankton composition at the beginning of each experiment 13
(each symbol). Filled and open symbols represent samples from coastal and oceanic 14
station, respectively, numbers correspond to the sampling station, triangles and circles 15
represent samples from surface and SCM, respectively, and colours correspond to the 16
months: (green) February, (blue) April and (pink) August. 17
Figure S2: Response ratio (RR) to inorganic nutrient addition (averaged biomass at the 18
end of the experiments by the averaged value in the control) of total phytoplankton 19
community (smooth bars) and of bacterial biomass (striped bars) at (a) coastal and (b) 20
oceanic station. Each bar corresponds to one of the 3 experiments (a, b or c) performed 21
in each depth and station during February, April and August. Colours represent samples 22
from (light grey) surface and (dark grey) SCM. Horizontal line represents a response 23
equal to 1, that means no change relative to control. Asterisks indicate phytoplankton 24
significant response relative to control (t-test; * p < 0.05) and circle indicate bacterial 25
significant response relative to the control (t-test; 0 p < 0.05). Note that different scales 26
were used. 27
Figure S3: Response ratio (RR) of total phytoplankton community (smooth bars) and of 28
bacterial biomass (striped bars) at (a) surface and (b) SCM in the coastal station and at 29
(c) surface and (d) SCM in the oceanic waters. Treatments represented are: B12; B1; 30
B12+B1 in pink tones and I+B12/I; I+B1/I; I+B12+B1/I in green tones. Pink bars 31
represent primary responses to B vitamins and green bars represent secondary responses 32
to B vitamins. Horizontal line represents a response equal to 1, that means no change 33
relative to control in the primary responses, and no change relative to inorganic treatment 34
in the secondary responses. Asterisks indicate phytoplankton significant response (t-test; 35
4
* p < 0.05) and circle indicate bacterial significant response (t-test; º p < 0.05). Note that 36
different scales were used. 37
a b c a b c a b c a b c a b c a b ca b c a b c a b c a b c a b c a b c
ba
Surf
ace
SCM
3a3b
3c3a
3b3c
3a3b
3c
Surfa
ce
SCM
6a6b
6c6a
6b6c
6a6b
6c
dc